For interpolated discrete Fourier transform (IpDFT)-based phasor estimators, the out-of-band interference (OOBI) test is among the most challenging ones. The typical iterative-interpolated DFT (i-IpDFT) phasor estimator utilizes a two-step iterative framework to eliminate the effects of the negative frequency and OOBI. However, the speed of estimation is limited by the adopted frequency estimator and the redundant iterations. To this end, this article proposes a fast i-IpDFT (FiIpDFT) method for the phasor estimation of an OOBI contaminated signal, which utilizes the three-point IpDFT (I3pDFT) technique. The proposed method first applies a noniterative frequency, amplitude, and phase estimator to eliminate the negative frequency interference. Then, a straightforward formula and two-stop criterion are introduced to reduce the computational burden of the OOBI elimination process. The accuracy and effectiveness of the proposed FiIpDFT method are validated by simulations. These are designed, under steady and dynamic conditions, according to the requirements of the Standard IEC/IEEE 60255-118-1.

The interpolated discrete Fourier transform (IpDFT) is one of the most popular techniques to estimate the parameters of a damped real-valued sinusoidal signal (DRSS). However, its accuracy is affected by strong noise presence and short observation windows. To this end, this letter proposes a novel two-point IpDFT method, called I2pZDFT, for the parameter estimation of a DRSS. The proposed I2pZDFT uses the zero-padding technique to increase the sampling rate in the frequency domain. The conjugate symmetry and the parity of the zero-padded signal are utilized to eliminate the influence of the spectral leakage. Simulation results highlight that the proposed I2pZDFT outperforms the existing IpDFT-based methods in terms of noise immunity, especially in the case of observation windows as short as 0.5 ~ 1 cycles.

This paper is devoted to the smart grid era which requires not only knowledge in the field of electric energy but also of many other fields like information technology, communication, control and automation, education, and nanotechnology. This universal effort will be effective if and only if the management and control of energy includes reliable information from accurate measurement methods and voltage and current sensors.

The frequency shifting and filtering (FSF) algorithm, a variant of DFT, has the merit of high efficiency for frequency analysis thanks to its simple implementation in the time domain. However, the inevitable white noise injected by various factors leads to inaccurate frequency estimation in practical measurement. This paper investigates the influence of a stationary white noise on FSF-based frequency estimation of the power system. The variance expression of the frequency estimator is derived theoretically and compared to its unbiased Cramer-Rao Lower Bound (CRLB). The obtained results are validated by several computer simulations.

Intelligent classification of multiple power quality disturbances (PQDs) is a top priority in pollution control of the power grid. However, the large-scale application of renewable energy introduces lots of nonlinear and impact loads, which makes the PQDs more complex and challenges the effectiveness of conventional detection frameworks. In this article, a novel framework based on optimized Kaiser-window-based $S$ -transform (OKST) and multiple fusion convolutional neural network (MFCNN) is proposed to identify multiple complex PQDs. First, the OKST is used for the time–frequency positioning of PQDs, where an improved control function is proposed to meet different detection requirements of time–frequency. Additionally, the parameters of the control function are adjusted automatically using maximum energy concentration. Then, the MFCNN based on residual networks (ResNets) is further proposed to extract and classify these time–frequency features automatically. In MFCNN, feature information is fused using different convolution kernels at a two-dimensional level, which can effectively reduce information loss and improve classification performance. The network model is set up using the Pytorch platform, and the dataset containing 28 types of PQDs and 2 types of nonlinearly mixed PQDs is built to test our framework. The result shows that the proposed OKST-MFCNN obtains an average accuracy of 99.38% under the 20-dB noise level, which is more accurate and robust than some advanced PQDs detection frameworks. Moreover, the accuracy of 97.94% is achieved with satisfactory real-time performance in hardware platform experiments, proving its superior identification performance for complex PQDs.